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Having recently become the owner of a garden, this looks very interesting. I've been using the myGardenAnswers app, which purported to do the same thing. It's been singularly useless so far, though.
I'm bribable for the job... :)
When can we get this for identifying plants/animals/rocks/fungus/etc in the wild? Would love this on a phone when hiking.
Me too. I volunteered at Audubon for a decade. I've got all the books, charts. I hike and camp. I can't identify rocks, birds, fish, trees, clouds, etc to save my life. Square peg, round hole.
What I'm realizing is that it takes active participation and interest in indentifying things to really learn their names and their histories, not just a passing interest.
The Cornell Lab of Ornithology has a site [1] and an app that is amazing for identifying birds. My favorite feature is how it also lists birds that are similar, which makes it a lot easier to find the specific bird you're looking for. I then compare visual information with the auditory information of the birds calls to make a concrete identification.
When can we get this for identifying plants/animals/rocks/fungus/etc in the wild? Would love this on a phone when hiking.
this is made with convnets. so just point me to a dataset with a thousand examples per type, and I'll give you a working app in days. of course, finding and preparing that data is the real challenge
I am not sure I even know enough to ask the right questions on this. But what tools, languages, computing resources, etc do you use?
I trained my kids, first for mushrooms, then for flowers and trees. They are better then any app :). But definitely would love to have an app to confirm our findings sometimes.
Train them to find truffle and you got a business.
This product already exists. It's called Google Image Search. For instance, I was actually able to identify the second flower he takes a picture of in the video as Camellia Japonica simply from a screenshot of the video. I'd be blown away if they were able to assemble a better training set than Google. Kind of disappointing, as from the headline I was expecting something based on NIR spectroscopy, like the SciO molec…
Second, is probably a Camellia japonica... but could be also a Camellia x williamsii. And you need to know that there is a Camellia sazanqua also. A trained human can spot the "too big leaves for sazanqua" in miliseconds (or too much shiny, or suspiciously blue, or photoshopsly faked, etc) but this is not so easy for a program. Search image will not spot the differences and just let you with the most common option.
When can we get this for identifying plants/animals/rocks/fungus/etc in the wild? Would love this on a phone when hiking.
This is very cool. I've been thinking about something similar, but only about mushrooms. Some people in my country pick wild mushrooms and sometimes it's very difficult to distinguish between the edible and the poisonous types. Such application, if existed could save many lives. The same concept could be used for other plants that people gather, like herbs and wild berries.
> Fast forward to today - the tech is available and the team is assembled to make PlantSnap a reality. With a beta version completed, and 250,000 images in the database 250,000 images doesn't sound like a large enough training set to be effective on anything but the most common plants.
To help others understand why, the "Deep Learning" book, which summarizes current state-of-the-art DL, advises having at least 5000 samples (images) per class for OK performance (equal to non-DL approaches) and 50k to 100k for state-of-the-art. A class here would be a plant species. So even with 5k samples, the 250k image corpus would only have 50 species, using this rule of thumb. A good engineer could pick up DL an…
It's also nice that life naturally fits into nested hierarchies because of evolution. So if you can recognize what family it belongs to, then that narrows down the possible sub families it can belong to. That in turn narrows down the possible clades it can belong to, etc, which narrows down the exact species. You couldn't find a more perfect use case for hierarchical softmax!
This product already exists. It's called Google Image Search. For instance, I was actually able to identify the second flower he takes a picture of in the video as Camellia Japonica simply from a screenshot of the video. I'd be blown away if they were able to assemble a better training set than Google. Kind of disappointing, as from the headline I was expecting something based on NIR spectroscopy, like the SciO molec…
There are two problems with this focus. First, lots and lots of photos on internet are incorrectly identified. I had seen web pages telling you how to include some edible spice in your dishes, with a stock photo of a similar species (very poisonous) incorrectly tagged as yummy. Second, is probably a Camellia japonica... but could be also a Camellia x williamsii. And you need to know that there is a Camellia sazanqua…
When can we get this for identifying plants/animals/rocks/fungus/etc in the wild? Would love this on a phone when hiking.
I made it as an experiment, results vary and can be awfully wrong but it's funny to see the confusions the neural network can make. Funny until somebody eat a deadly mushroom because of me, I guess. I already put plenty of warnings, so fingers crossed, but it's hard to be sure it won't be misused.